Clovis Grinand

The fight against deforestation and forest degradation is now a major challenge for the preservation of global forest ecosystems. The remote sensing forest monitoring methods that are currently deployed are not always adapted to the Ivorian context because of the high cloud cover, diversity of shaded crops, and land clearing techniques. This study proposes a drone-based approach to assess intra-annual tree losses in the Bossématié classified forest. The method used is based on a detection analysis of tree losses in forest areas from a time series of aerial images acquired by drones from November 2018 to April 2019 on five sites in the studied forest. Based on photogrammetric models and photointerpretation, tree heights and tree crown sizes were estimated. Then, tree losses were detected based on the variation of tree heights during the study period. An analysis of the distribution of tree heights in Bossématié classified forest reveals that the maximum tree height was 65.06 m in November 2018 and 64.07 m in April 2019 with an average tree height of 34.29–37.00 m in November 2018 and 34.63–36.88 m in April 2019. The average tree crown area, meanwhile, was estimated to be 152 m². With an estimation accuracy of about 97%, these tree structural data indicate a minimum loss of 107 trees corresponding to a clearing area of 2 ha across all the surveyed sites from November 2018 to April 2019. This forest monitoring approach shows a considerable local loss of biodiversity and should be involved in the implementation of preservation, rehabilitation, and deployment strategies in an operational deforestation monitoring system in Côte d’Ivoire.

Madagascar is recognized both for its unparalleled biodiversity and the high level of threat suffered by this biodiversity, associated in particular with anthropogenic deforestation. Despite sustained efforts to fight poverty and curb deforestation, forest cover in Madagascar is rapidly decreasing. To try to explain why it is so difficult to stop deforestation in Madagascar, we analysed the recent deforestation process in Western Madagascar through satellite image analysis and field surveys. We show that deforestation has increased from less than 0.9%/yr on 2000–2010 to more than 2%/yr on 2010–2017. We identified two major causes of deforestation, which were not associated with subsistence agriculture: slash-and-burn agriculture for the cultivation of cash crops (maize and peanut), and uncontrolled fires to create open pasture. Maize production is mainly at the destination of the domestic market and is used in particular for livestock feeding. Peanut production has boomed since 2013 and more than half of it is now exported towards asiatic countries. The money earned by farmers is principally invested into zebu herd acquisition. Trade of agricultural commodities benefits several intermediaries, some of whom have political responsibilities thus creating conflicts of interest. On the other hand, agents from institutions in charge of the management of the protected areas have no means to enforce laws against deforestation. In the absence of an efficient strategy to stop deforestation, we predicted that 38-93% of the forest present in 2000 will have disappeared in 2050. Forest loss, apart from biodiversity loss and climate-change global issues, will be at the expense of local population. In order to stop deforestation, international aid should be used to improve local governance to enforce environmental laws and pressure should be put on trading companies to buy certified agricultural commodities that are not derived from deforestation.

Remote sensing observations such as normalized difference vegetation index (NDVI) trends can provide important insights into past and present land condition. However, they do not directly provide comprehensive information about our representation of land degradation and the processes at work. This study aimed to analyze vegetation productivity underlying factors in order to assess land degradation and to highlight the impact of definitions on its quantitative assessment, using Mozambique as case‐study. Land productivity change were first analyzed using NDVI time‐series (2000–2016), and a two‐step framework was then used to understand the main factors of these productivity changes. The impact of land degradation's definition was assessed based on four types of stakeholder, with different priorities in terms of ecosystem services. The results show that 25% of the country display a significant land productivity decrease, while only 3% display a land productivity increase. A large part of these land productivity changes (>61% of the decrease, and >98% of the increase) is directly assigned to human activities, such as native forest growth or tree plantations (for the increase), or forest degradation, deforestation and loss of grassland productivity (for the decrease). We showed that the fraction of degraded land varies according to stakeholders' definitions, ranging from 12% to 20% of the Country, much less than the 39% estimated by Tier 1 United Nations Convention to Combat Desertification. This study provides a sound methodological framework for assessing land degradation status that could help stakeholders to design national and locally relevant land degradation mitigation policies or programmes.

Land degradation and regeneration are complex processes that greatly impact climate regulation, ecosystem service provision, and population well‐being and require an urgent and appropriate response through land use planning and interventions. Spatially explicit land change models can greatly help decision makers, but traditional regression approaches fail to capture the nonlinearity and complex interactions of the underlying drivers. Our objective was to use a machine learning algorithm combined with high‐resolution data sets to provide simultaneous and spatial forecasts of deforestation, land degradation, and regeneration for the next two decades. A 17,000‐km² region in the south of Madagascar was taken as the study area. First, an empirical analysis of drivers of change was conducted, and then, an ensemble model was calibrated to predict and map potential changes based on 12 potential explanatory variables. These potential change maps were used to draw three scenarios of land change while considering past trends in intensity of change and expert knowledge. Historical observations displayed clear patterns of land degradation and relatively low regeneration. Amongst the 12 potential explanatory variables, distance to forest edge and elevation were the most important for the three land transitions studied. Random forest showed slightly better prediction ability compared with maximum entropy and generalized linear model. Business‐as‐usual scenarios highlighted the large areas under deforestation and degradation threat, and an alternative scenario enabled the location of suitable areas for regeneration. The approach developed herein and the spatial outputs provided can help stakeholders target their interventions or develop large‐scale sustainable land management strategies.

The island of Madagascar has a unique biodiversity, mainly located in the tropical forests of the island. This biodiversity is highly threatened by anthropogenic deforestation. Existing historical forest maps at national level are scattered and have substantial gaps which prevent an exhaustive assessment of long-term deforestation trends in Madagascar. In this study, we combined historical national forest cover maps (covering the period 1953-2000) with a recent global annual tree cover loss dataset (2001-2014) to look at six decades of deforestation and forest fragmentation in Madagascar (from 1953 to 2014). We produced new forest cover maps at 30 m resolution for the year 1990 and annually from 2000 to 2014 over the full territory of Madagascar. We estimated that Madagascar has lost 44% of its natural forest cover over the period 1953-2014 (including 37% over the period 1973-2014). Natural forests cover 8.9 Mha in 2014 (15% of the national territory) and include 4.4 Mha (50%) of moist forests, 2.6 Mha (29%) of dry forests, 1.7 Mha of spiny forests (19%) and 177,000 ha (2%) of mangroves. Since 2005, the annual deforestation rate has progressively increased in Madagascar to reach 99,000 ha/yr during 2010-2014 (corresponding to a rate of 1.1%/yr). Around half of the forest (46%) is now located at less than 100 m from the forest edge. Our approach could be replicated to other developing countries with tropical forest. Accurate forest cover change maps can be used to assess the effectiveness of past and current conservation programs and implement new strategies for the future. In particular, forest maps and estimates can be used in the REDD+ framework which aims at “Reducing Emissions from Deforestation and forest Degradation” and for optimizing the current protected area network.

Summary: Recent studies have underlined the importance of climatic variables in determining tree height and biomass in tropical forests. Nonetheless, the effects of climate on tropical forest carbon stocks remain uncertain. In particular, the application of process‐based dynamic global vegetation models has led to contrasting conclusions regarding the potential impact of climate change on tropical forest carbon storage. Using a correlative approach based on a bioclimatic envelope model and data from 1771 forest plots inventoried during the period 1996–2013 in Madagascar over a large climatic gradient, we show that temperature seasonality, annual precipitation and mean annual temperature are key variables in determining forest above‐ground carbon density. Taking into account the explicative climate variables, we obtained an accurate (R² = 70% and RMSE = 40 Mg ha⁻¹) forest carbon map for Madagascar at 250 m resolution for the year 2010. This national map was more accurate than previously published global carbon maps (R² ≤ 26% and RMSE ≥ 63 Mg ha⁻¹). Combining our model with the climatic projections for Madagascar from 7 IPCC CMIP5 global climate models following the RCP 8.5, we forecast an average forest carbon stock loss of 17% (range: 7–24%) by the year 2080. For comparison, a spatially homogeneous deforestation of 0.5% per year on the same period would lead to a loss of 30% of the forest carbon stock. Synthesis. Our study shows that climate change is likely to induce a decrease in tropical forest carbon stocks. This loss could be due to a decrease in the average tree size and to shifts in tree species distribution, with the selection of small‐statured species. In Madagascar, climate‐induced carbon emissions might be, at least, of the same order of magnitude as emissions associated with anthropogenic deforestation.